As the development of medical imaging technology, image processing methods have been widely used in medical research and clinical applications, such as radiotherapy planning, intervention treatment, and surgical guidance. Image segmentation is one of the important topic in medical image processing. It can help the clinician to segment and extract the region of interest (pathological tissues etc.) for further analyzing and quantification, which can increase the accuracy and efficiency of clinical diagnosis. However, because of the variety and complexity of the medical images, image segmentation poses a great challenge.
Among the image segmentation method used clinically, thresholding method is the most widely used because of its simple implementation and small computational load. Its algorithm is to set different threshold based on different image features and grouping the image pixels into several categories. Mostly used features include grayscale values of the image, color image features, or the features transformed from the original gray scale images or color images. However, when there are many different soft tissues in one image with low contrast, the thresholding method could not segment the images. Also, it is very sensitive to noise. Therefore, it is usually used to segment blood cells or CT images but not all types of images or soft tissues.
Other image segmentation method include template-based method, which needs additional contour template information. This could not be used to segment soft tissues which has a relatively large difference or deformation compared to template and those which are not included in the template.